Team clearly described the dataset and clearly described the motivation behind studying the data. Team provided scholarly citations or quantitative facts to describe the motivation.
Team clearly described their data cleaning and outlier removal process. Team presented insightful visualizations motivating to do further exploratory or confirmatory analysis.
#PART 1: Read csv, merge, clean and plot outliers.
library(readr)
library(readxl)
library(dplyr)
library(countrycode)
library(car)
source('Read_Clean.R')
cleaned <- Read_Clean()
#After Cleaning, check how many NA values are in dataset.
# cleaned %>%
# select(everything()) %>%
# summarise_all(funs(sum(is.na(.)))) %>% rowSums()
Team applied dimension reduction analysis correctly and discussed the motivation behind that. Also, they provided interesting insights into the results.
Part A: MDS
#PART 2: MDS
image
# PART 3: PCA
library(pryr)
library(ggbiplot) #if the library is not present use the code below
#library(devtools)
#install_github("vqv/ggbiplot")
source('PCA.R')
(PrinCompPlot <- PCA(cleaned))
# PART 3: Hierarchical Clustering between Continents
library(ape)
source('cluster_continents.R')
Cl_continents <- cluster_continents(cleaned)
# PART 4: K-means & Model Based Clustering between Countries
library(mclust)
library(maptools)
source('clusters_countries.R')
Cl_countries <- clusters_countries(cleaned)
#Show Centers
#PART 5: EFA
#PART 6: CFA